Differential phospho analysis - CRC Batch 2 Set 4
Sophie Herbst
Analysis date: 2023-10-18
Depends on
CRC_Xenografts_Batch2_DataProcessing Script
load("../Data/Cache/Xenografts_Batch2_DataProcessing.RData")TODO
Setup
Load libraries and functions
Analysis
DEP
Tyrosine
E vs ctrl
data_diff_E_vs_ctrl_pY <- test_diff(pY_se_Set4, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_E_vs_ctrl_pY <- add_rejections_SH(data_diff_E_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_E_vs_ctrl_pY, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pY") Return_DEP_Hits_Plots(data = pY_Set4_form, dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns## Loading required namespace: reactome.db## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## [1] "Signal Transduction"
## [2] "Insulin receptor signalling cascade"
## [3] "GPCR downstream signalling"
## [4] "Golgi Cisternae Pericentriolar Stack Reorganization"
## [5] "Tight junction interactions"PTM-SEA
GSEA_E_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PERT-PSP_ERLOTINIB" "PATH-NP_EGFR1_PATHWAY"
## [3] "PERT-PSP_FGF1" "PERT-PSP_IMATINIB"
## [5] "PERT-PSP_PAR1_ACTIVATING_PEPTIDE" "PERT-P100-DIA2_VEMURAFENIB"
## [7] "KINASE-PSP_EphA2/EPHA2"GSEA_E_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))## # A tibble: 67 × 8
## pathway pval padj log2err ES NES size leadingEdge
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <list>
## 1 PERT-PSP_ERLOTINIB 1.25e-5 0.00222 0.593 0.846 2.12 8 <chr [7]>
## 2 PATH-NP_EGFR1_PATHWAY 7.06e-6 0.00222 0.611 0.432 1.97 110 <chr [48]>
## 3 PERT-PSP_IL_2 1.36e-3 0.0101 0.455 0.794 1.82 6 <chr [4]>
## 4 PERT-PSP_FGF1 8.49e-5 0.00436 0.538 0.976 1.77 3 <chr [3]>
## 5 PERT-PSP_IMATINIB 8.49e-5 0.00436 0.538 0.976 1.77 3 <chr [3]>
## 6 KINASE-iKiP_EGFR 1.58e-3 0.0115 0.455 0.734 1.75 7 <chr [4]>
## 7 PERT-PSP_PAR1_ACTIVATI… 3.43e-4 0.00436 0.498 0.959 1.74 3 <chr [3]>
## 8 PATH-NP_TSLP_PATHWAY 5.94e-3 0.0400 0.407 0.603 1.72 12 <chr [7]>
## 9 PERT-PSP_AG1478 1.60e-3 0.0115 0.455 0.927 1.68 3 <chr [2]>
## 10 KINASE-iKiP_PDGFRA 8.73e-3 0.0465 0.381 0.745 1.61 5 <chr [3]>
## # ℹ 57 more rowsRun_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PERT-PSP_ERLOTINIB" "PATH-NP_EGFR1_PATHWAY"
## [3] "PERT-PSP_FGF1" "PERT-PSP_IMATINIB"
## [5] "PERT-P100-DIA2_VEMURAFENIB" "KINASE-PSP_EphA2/EPHA2"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 250 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 CDK1 IGEGTYGVVyKGR Y19-p 1.78
## 2 MAPK3 IADPEHDHTGFLTEyVATR Y204-p 1.36
## 3 MAPK3 IADPEHDHTGFLtEyVATR Y204-p 1.36
## 4 MAPK3 iADPEHDHTGFLTEyVATR Y204-p 1.36
## 5 PTK6 ERLSSFTSyENPT Y447-p 1.31
## 6 PTK6 LSSFTSyENPT Y447-p 1.31
## 7 PTK6 eRLSSFTSyENPT Y447-p 1.31
## 8 PTK6 lSSFTSyENPT Y447-p 1.31
## 9 PTPRA VVQEYIDAFSDyANFK Y798-p 1.23
## 10 PTPRA vVQEYIDAFSDyANFk Y798-p 1.23
## # ℹ 240 more rowsRun_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PERT-PSP_ERLOTINIB" "PATH-NP_EGFR1_PATHWAY"
## [3] "PERT-PSP_FGF1" "PERT-PSP_IMATINIB"
## [5] "PERT-PSP_PAR1_ACTIVATING_PEPTIDE" "PERT-P100-DIA2_VEMURAFENIB"
## [7] "KINASE-PSP_EphA2/EPHA2"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 81 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 CDH1 yLPRPANPDEIGNFIDENLK Y797-p 1.25
## 2 CDH1 yLPRPANPDEIGNFIDENLk Y797-p 1.25
## 3 PTPRA VVQEYIDAFSDyANFK Y798-p 1.23
## 4 PTPRA vVQEYIDAFSDyANFk Y798-p 1.23
## 5 PAG1 SREEDPTLTEEEISAMySSVNKPGQLVNK Y317-p 1.20
## 6 PAG1 sREEDPTLTEEEISAMySSVNkPGQLVNk Y317-p 1.20
## 7 CTTN NASTFEDVTQVSSAyQK Y334-p 1.15
## 8 CTTN MDKNASTFEDVTQVSSAyQK Y334-p 1.15
## 9 CTTN nASTFEDVTQVSSAyQk Y334-p 1.15
## 10 SHC1 ELFDDPSyVNVQNLDK Y427-p 1.11
## # ℹ 71 more rowsRun_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PERT-PSP_ERLOTINIB" "PATH-NP_EGFR1_PATHWAY"
## [3] "PERT-PSP_FGF1" "PERT-PSP_IMATINIB"
## [5] "KINASE-iKiP_EGFR" "PERT-PSP_PAR1_ACTIVATING_PEPTIDE"
## [7] "PERT-P100-DIA2_VEMURAFENIB" "KINASE-PSP_EphA2/EPHA2"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 14 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 EPHA2 VLEDDPEATyTTSGGK Y772-p 0.316
## 2 EPHA2 VLEDDPEATyTTSGGKIPIR Y772-p 0.316
## 3 EPHA2 vLEDDPEATyTTSGGk Y772-p 0.316
## 4 EPHA2 vLEDDPEATyTTSGGkIPIR Y772-p 0.316
## 5 EPHA2 TyVDPHTYEDPNQAVLK Y588-p 0.258
## 6 EPHA2 VIGAGEFGEVyKGMLK Y628-p 0.199
## 7 EPHA2 QSPEDVyFSK Y575-p 0.0356
## 8 EPHA2 qSPEDVyFSk Y575-p 0.0356
## 9 EPHA2 TYVDPHTyEDPNQAVLK Y594-p -0.00166
## 10 EPHA2 tYVDPHTyEDPNQAVLk Y594-p -0.00166
## 11 EPHA2 YLANMNyVHR Y735-p -0.205
## 12 EPHA2 IAySLLGLK Y960-p -0.576
## 13 CLDN4 SAAASNyV Y208-p -1.21
## 14 CLDN4 sAAASNyV Y208-p -1.21EC vs ctrl
data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set4, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pY") Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## character(0)Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")PTM-SEA
GSEA_EC_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "KINASE-PSP_EphA2/EPHA2"GSEA_EC_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))## # A tibble: 1 × 8
## pathway pval padj log2err ES NES size leadingEdge
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <list>
## 1 KINASE-PSP_EphA2/EPHA2 0.0000345 0.0123 0.557 -0.887 -2.16 8 <chr [7]>Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "KINASE-PSP_EphA2/EPHA2"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 250 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 ENO1 AAVPSGASTGIyEALELRDNDK Y44-p 1.27
## 2 ENO1 AAVPSGASTGIyEALELRDNDKTR Y44-p 1.27
## 3 ENO1 aAVPSGASTGIyEALELRDNDk Y44-p 1.27
## 4 PTPRA VVQEYIDAFSDyANFK Y798-p 1.08
## 5 PTPRA vVQEYIDAFSDyANFk Y798-p 1.08
## 6 DLG3 DNEVDGQDyHFVVSR Y673-p 1.04
## 7 DLG3 RDNEVDGQDyHFVVSR Y673-p 1.04
## 8 DLG3 dNEVDGQDyHFVVSR Y673-p 1.04
## 9 DLG3 rDNEVDGQDyHFVVSR Y673-p 1.04
## 10 CTTN LPSSPVyEDAASFK Y421-p 1.03
## # ℹ 240 more rowsRun_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "KINASE-PSP_EphA2/EPHA2"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 81 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 PTPRA VVQEYIDAFSDyANFK Y798-p 1.08
## 2 PTPRA vVQEYIDAFSDyANFk Y798-p 1.08
## 3 ARHGAP35 SVSSSPWLPQDGFDPSDyAEPMDAVVKPR Y1087-p 1.06
## 4 ARHGAP35 sVSSSPWLPQDGFDPSDyAEPMDAVVkPR Y1087-p 1.06
## 5 CTTN NASTFEDVTQVSSAyQK Y334-p 1.04
## 6 CTTN MDKNASTFEDVTQVSSAyQK Y334-p 1.04
## 7 CTTN nASTFEDVTQVSSAyQk Y334-p 1.04
## 8 ARHGAP35 NEEENIySVPHDSTQGK Y1105-p 0.966
## 9 ARHGAP35 nEEENIySVPHDSTQGk Y1105-p 0.966
## 10 SRC EPEERPTFEYLQAFLEDYFTSTEPQyQPGENL Y530-p 0.880
## # ℹ 71 more rowsRun_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "KINASE-PSP_EphA2/EPHA2"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 14 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 EPHA2 VIGAGEFGEVyKGMLK Y628-p -0.155
## 2 EPHA2 YLANMNyVHR Y735-p -0.486
## 3 EPHA2 TYVDPHTyEDPNQAVLK Y594-p -0.526
## 4 EPHA2 tYVDPHTyEDPNQAVLk Y594-p -0.526
## 5 EPHA2 VLEDDPEATyTTSGGK Y772-p -0.576
## 6 EPHA2 VLEDDPEATyTTSGGKIPIR Y772-p -0.576
## 7 EPHA2 vLEDDPEATyTTSGGk Y772-p -0.576
## 8 EPHA2 vLEDDPEATyTTSGGkIPIR Y772-p -0.576
## 9 EPHA2 TyVDPHTYEDPNQAVLK Y588-p -0.712
## 10 EPHA2 QSPEDVyFSK Y575-p -0.786
## 11 EPHA2 qSPEDVyFSk Y575-p -0.786
## 12 EPHA2 IAySLLGLK Y960-p -1.57
## 13 CLDN4 SAAASNyV Y208-p -1.79
## 14 CLDN4 sAAASNyV Y208-p -1.79EBC vs ctrl
data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set4, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## character(0)PTM-SEA
GSEA_EBC_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "KINASE-PSP_EphA2/EPHA2"GSEA_EBC_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))## # A tibble: 1 × 8
## pathway pval padj log2err ES NES size leadingEdge
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <list>
## 1 KINASE-PSP_EphA2/EPHA2 1.28e-5 0.00457 0.593 -0.858 -2.67 8 <chr [6]>Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "KINASE-PSP_EphA2/EPHA2"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 250 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 CTTN LPSSPVyEDAASFK Y421-p 1.52
## 2 CTTN lPSSPVyEDAASFk Y421-p 1.52
## 3 CTTN TQTPPVSPAPQPTEERLPSSPVyEDAASFK Y421-p 1.52
## 4 NCK1 LyDLNMPAYVK Y105-p 1.50
## 5 NCK1 lyDLNMPAYVk Y105-p 1.50
## 6 PTK6 ERLSSFTSyENPT Y447-p 1.35
## 7 PTK6 LSSFTSyENPT Y447-p 1.35
## 8 PTK6 eRLSSFTSyENPT Y447-p 1.35
## 9 PTK6 lSSFTSyENPT Y447-p 1.35
## 10 ENO1 AAVPSGASTGIyEALELRDNDK Y44-p 1.33
## # ℹ 240 more rowsRun_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "KINASE-PSP_EphA2/EPHA2"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 81 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 CTTN NASTFEDVTQVSSAyQK Y334-p 1.50
## 2 CTTN MDKNASTFEDVTQVSSAyQK Y334-p 1.50
## 3 CTTN nASTFEDVTQVSSAyQk Y334-p 1.50
## 4 CDH1 yLPRPANPDEIGNFIDENLK Y797-p 1.41
## 5 CDH1 yLPRPANPDEIGNFIDENLk Y797-p 1.41
## 6 PTPRA VVQEYIDAFSDyANFK Y798-p 1.17
## 7 PTPRA vVQEYIDAFSDyANFk Y798-p 1.17
## 8 DAPP1 KVEEPSIyESVR Y139-p 1.01
## 9 CLTC SVNESLNNLFITEEDyQALR Y1477-p 0.986
## 10 CLTC sVNESLNNLFITEEDyQALR Y1477-p 0.986
## # ℹ 71 more rowsRun_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "KINASE-PSP_EphA2/EPHA2"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 14 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 EPHA2 VIGAGEFGEVyKGMLK Y628-p 0.191
## 2 EPHA2 TyVDPHTYEDPNQAVLK Y588-p -0.0782
## 3 EPHA2 VLEDDPEATyTTSGGK Y772-p -0.296
## 4 EPHA2 VLEDDPEATyTTSGGKIPIR Y772-p -0.296
## 5 EPHA2 vLEDDPEATyTTSGGk Y772-p -0.296
## 6 EPHA2 vLEDDPEATyTTSGGkIPIR Y772-p -0.296
## 7 EPHA2 TYVDPHTyEDPNQAVLK Y594-p -0.310
## 8 EPHA2 tYVDPHTyEDPNQAVLk Y594-p -0.310
## 9 EPHA2 YLANMNyVHR Y735-p -0.375
## 10 EPHA2 QSPEDVyFSK Y575-p -0.382
## 11 EPHA2 qSPEDVyFSk Y575-p -0.382
## 12 EPHA2 IAySLLGLK Y960-p -0.969
## 13 CLDN4 SAAASNyV Y208-p -1.25
## 14 CLDN4 sAAASNyV Y208-p -1.25EC vs E
data_diff_EC_vs_E_pY <- test_diff(pY_se_Set4, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, contrast = "EC_vs_E", add_names = TRUE, additional_title = "pY", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## [1] "SHC1 events in EGFR signaling"
## [2] "Golgi Cisternae Pericentriolar Stack Reorganization"
## [3] "G alpha (q) signalling events"
## [4] "Insulin receptor signalling cascade"#data_results <- get_df_long(dep)PTM-SEA
GSEA_EC_vs_E_PTM <- Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PATH-NP_EGFR1_PATHWAY" "KINASE-iKiP_EGFR" "PERT-PSP_ERLOTINIB"GSEA_EC_vs_E_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))## # A tibble: 4 × 8
## pathway pval padj log2err ES NES size leadingEdge
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <list>
## 1 PATH-NP_EGFR1_PATHWAY 0.000128 0.0229 0.519 -0.472 -1.60 110 <chr [55]>
## 2 KINASE-iKiP_EGFR 0.000295 0.0264 0.498 -0.861 -1.82 7 <chr [5]>
## 3 KINASE-PSP_EGFR 0.000294 0.0264 0.498 -0.840 -1.85 8 <chr [5]>
## 4 PERT-PSP_ERLOTINIB 0.0000157 0.00562 0.576 -0.892 -1.97 8 <chr [7]>Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PERT-P100-DIA2_VEMURAFENIB" "PATH-NP_EGFR1_PATHWAY"
## [3] "PERT-PSP_ANTI_CD3" "KINASE-iKiP_EGFR"
## [5] "PERT-PSP_ERLOTINIB"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 250 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 NCK1 LyDLNMPAYVK Y105-p 0.260
## 2 NCK1 lyDLNMPAYVk Y105-p 0.260
## 3 PIK3R1 DQyLMWLTQK Y580-p 0.238
## 4 PIK3R1 TRDQyLMWLTQK Y580-p 0.238
## 5 PIK3R1 dQyLMWLTQk Y580-p 0.238
## 6 PIK3R1 dQyLmWLTQk Y580-p 0.238
## 7 SRC EPEERPTFEYLQAFLEDYFTSTEPQyQPGENL Y530-p 0.223
## 8 SRC kEPEERPTFEYLQAFLEDYFTSTEPQyQPGENL Y530-p 0.223
## 9 STAT3 YCRPESQEHPEADPGSAAPyLK Y705-p 0.217
## 10 ENO1 AAVPSGASTGIyEALELRDNDK Y44-p 0.185
## # ℹ 240 more rowsRun_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PATH-NP_EGFR1_PATHWAY" "PERT-PSP_ANTI_CD3" "KINASE-iKiP_EGFR"
## [4] "PERT-PSP_ERLOTINIB"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 81 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 ARHGAP35 SVSSSPWLPQDGFDPSDyAEPMDAVVKPR Y1087-p 0.918
## 2 ARHGAP35 sVSSSPWLPQDGFDPSDyAEPMDAVVkPR Y1087-p 0.918
## 3 ARHGAP35 NEEENIySVPHDSTQGK Y1105-p 0.444
## 4 ARHGAP35 nEEENIySVPHDSTQGk Y1105-p 0.444
## 5 SDCBP VDKVIQAQTAFSANPANPAILSEASAPIPHDGNLyPR Y46-p 0.231
## 6 SDCBP VIQAQTAFSANPANPAILSEASAPIPHDGNLyPR Y46-p 0.231
## 7 SDCBP vDkVIQAQTAFSANPANPAILSEASAPIPHDGNLyPR Y46-p 0.231
## 8 SDCBP vIQAQTAFSANPANPAILSEASAPIPHDGNLyPR Y46-p 0.231
## 9 SRC EPEERPTFEYLQAFLEDYFTSTEPQyQPGENL Y530-p 0.223
## 10 SRC kEPEERPTFEYLQAFLEDYFTSTEPQyQPGENL Y530-p 0.223
## # ℹ 71 more rowsRun_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## [1] "PATH-NP_EGFR1_PATHWAY" "KINASE-iKiP_EGFR" "PERT-PSP_ERLOTINIB"## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 14 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 EPHA2 YLANMNyVHR Y735-p -0.281
## 2 EPHA2 VIGAGEFGEVyKGMLK Y628-p -0.354
## 3 EPHA2 TYVDPHTyEDPNQAVLK Y594-p -0.525
## 4 EPHA2 tYVDPHTyEDPNQAVLk Y594-p -0.525
## 5 CLDN4 SAAASNyV Y208-p -0.580
## 6 CLDN4 sAAASNyV Y208-p -0.580
## 7 EPHA2 QSPEDVyFSK Y575-p -0.822
## 8 EPHA2 qSPEDVyFSk Y575-p -0.822
## 9 EPHA2 VLEDDPEATyTTSGGK Y772-p -0.892
## 10 EPHA2 VLEDDPEATyTTSGGKIPIR Y772-p -0.892
## 11 EPHA2 vLEDDPEATyTTSGGk Y772-p -0.892
## 12 EPHA2 vLEDDPEATyTTSGGkIPIR Y772-p -0.892
## 13 EPHA2 TyVDPHTYEDPNQAVLK Y588-p -0.970
## 14 EPHA2 IAySLLGLK Y960-p -0.996EBC vs EC
data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set4, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set4_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 1 pathways for which P-values were not calculated
## properly due to unbalanced (positive and negative) gene-level statistic values.
## For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple
## = 10000)## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some of the pathways the P-values were likely overestimated. For
## such pathways log2err is set to NA.## [1] "Cell-Cell communication"
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)PTM-SEA
GSEA_EBC_vs_EC_PTM <- Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)GSEA_EBC_vs_EC_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))## # A tibble: 0 × 8
## # ℹ 8 variables: pathway <chr>, pval <dbl>, padj <dbl>, log2err <dbl>,
## # ES <dbl>, NES <dbl>, size <int>, leadingEdge <list>Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 250 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 LCK SVLEDFFTATEGQyQPQP Y505-p 1.51
## 2 LCK sVLEDFFTATEGQyQPQP Y505-p 1.51
## 3 WASL VIyDFIEK Y256-p 0.892
## 4 WASL ETSKVIyDFIEK Y256-p 0.892
## 5 WASL eTSkVIyDFIEk Y256-p 0.892
## 6 WASL vIyDFIEk Y256-p 0.892
## 7 CTNND1 APSRQDVyGPQPQVR Y257-p 0.859
## 8 CTNND1 APsRQDVyGPQPQVR Y257-p 0.859
## 9 CTNND1 QDVyGPQPQVR Y257-p 0.859
## 10 PEAK1 VPIVINPNAyDNLAIYK Y635-p 0.762
## # ℹ 240 more rowsRun_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 81 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 CFL1 NIILEEGKEILVGDVGQTVDDPyATFVK Y68-p 0.884
## 2 CFL1 nIILEEGkEILVGDVGQTVDDPyATFVk Y68-p 0.884
## 3 SHC1 ELFDDPSyVNVQNLDK Y427-p 0.722
## 4 SHC1 eLFDDPSyVNVQNLDk Y427-p 0.722
## 5 DAPP1 KVEEPSIyESVR Y139-p 0.675
## 6 EGFR YSSDPTGALTEDSIDDTFLPVPEyINQSVPK Y1092-p 0.616
## 7 PRKCD KTGVAGEDMQDNSGTyGK Y334-p 0.603
## 8 PRKCD TGVAGEDMQDNSGTyGK Y334-p 0.603
## 9 PRKCD tGVAGEDMQDNSGTyGk Y334-p 0.603
## 10 CDH1 yLPRPANPDEIGNFIDENLK Y797-p 0.583
## # ℹ 71 more rowsRun_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`## character(0)## Joining with `by = join_by(SITE_GRP_ID)`
## # A tibble: 14 × 4
## HGNC_Symbol Annotated_Sequence MOD_RSD FC
## <chr> <chr> <chr> <dbl>
## 1 EPHA2 TyVDPHTYEDPNQAVLK Y588-p 0.634
## 2 EPHA2 IAySLLGLK Y960-p 0.602
## 3 CLDN4 SAAASNyV Y208-p 0.540
## 4 CLDN4 sAAASNyV Y208-p 0.540
## 5 EPHA2 QSPEDVyFSK Y575-p 0.404
## 6 EPHA2 qSPEDVyFSk Y575-p 0.404
## 7 EPHA2 VIGAGEFGEVyKGMLK Y628-p 0.346
## 8 EPHA2 VLEDDPEATyTTSGGK Y772-p 0.280
## 9 EPHA2 VLEDDPEATyTTSGGKIPIR Y772-p 0.280
## 10 EPHA2 vLEDDPEATyTTSGGk Y772-p 0.280
## 11 EPHA2 vLEDDPEATyTTSGGkIPIR Y772-p 0.280
## 12 EPHA2 TYVDPHTyEDPNQAVLK Y594-p 0.216
## 13 EPHA2 tYVDPHTyEDPNQAVLk Y594-p 0.216
## 14 EPHA2 YLANMNyVHR Y735-p 0.111Serine/Threonine all
Each condition vs ctrl
data_diff_E_vs_ctrl_pST <- test_diff(pST_se_Set4, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_E_vs_ctrl_pST <- add_rejections_SH(data_diff_E_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_E_vs_ctrl_pST, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set4_form, dep_E_vs_ctrl_pST, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## character(0)data_diff_EC_vs_ctrl_pST <- test_diff(pST_se_Set4, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pST <- add_rejections_SH(data_diff_EC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## character(0)Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pST <- test_diff(pST_se_Set4, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pST <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EBC_vs_ctrl_pST, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## character(0)EC vs E
data_diff_EC_vs_E_pST <- test_diff(pST_se_Set4, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_E## Warning in pvt.fit.nullmodel(x, x0, statistic = statistic): Variance of scale
## parameter set to zero due to numerical problemsdep_EC_vs_E_pST <- add_rejections_SH(data_diff_EC_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST, contrast = "EC_vs_E", add_names = TRUE, additional_title = "pST", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EC_vs_E_pST, comparison = "EC_vs_E_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## character(0)#data_results <- get_df_long(dep)EBC vs EC
data_diff_EBC_vs_EC_pST <- test_diff(pST_se_Set4, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pST <- add_rejections_SH(data_diff_EBC_vs_EC_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set4_form, dep_EBC_vs_EC_pST, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## character(0)
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)Save
rowData(dep_E_vs_ctrl_pY) %>% as_tibble() %>% select(HGNC_Symbol, E_vs_ctrl_diff) %>% write.table("../Data/Kinase_enrichment/Batch2_Set4_E_vs_ctrl_pY_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")
rowData(dep_EC_vs_ctrl_pY) %>% as_tibble() %>% select(HGNC_Symbol, EC_vs_ctrl_diff) %>% write.table("../Data/Kinase_enrichment/Batch2_Set4_EC_vs_ctrl_pY_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")
rowData(dep_E_vs_ctrl_pY) %>% as_tibble() %>%
select(HGNC_Symbol, ends_with("_diff")) %>%
group_by(HGNC_Symbol) %>%
mutate(abs_FC = abs(E_vs_ctrl_diff) ) %>%
arrange(desc( abs_FC) ) %>%
slice(1) %>%
ungroup() %>%
select(HGNC_Symbol, ends_with("_diff") ) %>%
write.table("../Data/Kinase_enrichment/Batch2_Set4_E_vs_ctrl_pY_mostextremeFCperprotein_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")
rowData(dep_EC_vs_ctrl_pY) %>% as_tibble() %>%
select(HGNC_Symbol, ends_with("_diff")) %>%
group_by(HGNC_Symbol) %>%
mutate(abs_FC = abs(EC_vs_ctrl_diff) ) %>%
arrange(desc( abs_FC) ) %>%
slice(1) %>%
ungroup() %>%
select(HGNC_Symbol, ends_with("_diff") ) %>%
write.table("../Data/Kinase_enrichment/Batch2_Set4_EC_vs_ctrl_pY_mostextremeFCperprotein_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")
rowData(dep_E_vs_ctrl_pY) %>% as_tibble() %>%
filter(E_vs_ctrl_diff>1) %>%
select(HGNC_Symbol ) %>% unique() %>%
write.table("../Data/Kinase_enrichment/Batch2_Set4_E_vs_ctrl_pY_FCmorethan1_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")
rowData(dep_EC_vs_ctrl_pY) %>% as_tibble() %>%
filter(EC_vs_ctrl_diff>1) %>%
select(HGNC_Symbol ) %>% unique() %>%
write.table("../Data/Kinase_enrichment/Batch2_Set4_EC_vs_ctrl_pY_FCmorethan1_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")Session Info
sessionInfo()## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] lubridate_1.9.2 forcats_1.0.0
## [3] stringr_1.5.0 dplyr_1.1.2
## [5] purrr_1.0.2 readr_2.1.4
## [7] tidyr_1.3.0 tibble_3.2.1
## [9] ggplot2_3.4.2 tidyverse_2.0.0
## [11] mdatools_0.14.0 SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
## [15] MatrixGenerics_1.10.0 matrixStats_1.0.0
## [17] DEP_1.20.0 org.Hs.eg.db_3.16.0
## [19] AnnotationDbi_1.60.2 IRanges_2.32.0
## [21] S4Vectors_0.36.2 Biobase_2.58.0
## [23] BiocGenerics_0.44.0 fgsea_1.24.0
##
## loaded via a namespace (and not attached):
## [1] circlize_0.4.15 fastmatch_1.1-4 plyr_1.8.8
## [4] igraph_1.5.1 gmm_1.8 lazyeval_0.2.2
## [7] shinydashboard_0.7.2 crosstalk_1.2.0 BiocParallel_1.32.6
## [10] digest_0.6.33 foreach_1.5.2 htmltools_0.5.6
## [13] fansi_1.0.4 magrittr_2.0.3 memoise_2.0.1
## [16] cluster_2.1.4 doParallel_1.0.17 tzdb_0.4.0
## [19] limma_3.54.2 ComplexHeatmap_2.14.0 Biostrings_2.66.0
## [22] imputeLCMD_2.1 sandwich_3.0-2 timechange_0.2.0
## [25] colorspace_2.1-0 blob_1.2.4 xfun_0.40
## [28] crayon_1.5.2 RCurl_1.98-1.12 jsonlite_1.8.7
## [31] impute_1.72.3 zoo_1.8-12 iterators_1.0.14
## [34] glue_1.6.2 hash_2.2.6.2 gtable_0.3.3
## [37] zlibbioc_1.44.0 XVector_0.38.0 GetoptLong_1.0.5
## [40] DelayedArray_0.24.0 shape_1.4.6 scales_1.2.1
## [43] pheatmap_1.0.12 vsn_3.66.0 mvtnorm_1.2-2
## [46] DBI_1.1.3 Rcpp_1.0.11 plotrix_3.8-2
## [49] mzR_2.32.0 viridisLite_0.4.2 xtable_1.8-4
## [52] clue_0.3-64 reactome.db_1.82.0 bit_4.0.5
## [55] preprocessCore_1.60.2 sqldf_0.4-11 MsCoreUtils_1.10.0
## [58] DT_0.28 htmlwidgets_1.6.2 httr_1.4.6
## [61] gplots_3.1.3 RColorBrewer_1.1-3 ellipsis_0.3.2
## [64] farver_2.1.1 pkgconfig_2.0.3 XML_3.99-0.14
## [67] sass_0.4.7 utf8_1.2.3 STRINGdb_2.10.1
## [70] labeling_0.4.2 tidyselect_1.2.0 rlang_1.1.1
## [73] later_1.3.1 munsell_0.5.0 tools_4.2.3
## [76] cachem_1.0.8 cli_3.6.1 gsubfn_0.7
## [79] generics_0.1.3 RSQLite_2.3.1 fdrtool_1.2.17
## [82] evaluate_0.21 fastmap_1.1.1 mzID_1.36.0
## [85] yaml_2.3.7 knitr_1.43 bit64_4.0.5
## [88] caTools_1.18.2 KEGGREST_1.38.0 ncdf4_1.21
## [91] mime_0.12 compiler_4.2.3 rstudioapi_0.15.0
## [94] plotly_4.10.2 png_0.1-8 affyio_1.68.0
## [97] stringi_1.7.12 bslib_0.5.0 highr_0.10
## [100] MSnbase_2.24.2 lattice_0.21-8 ProtGenerics_1.30.0
## [103] Matrix_1.6-0 tmvtnorm_1.5 vctrs_0.6.3
## [106] pillar_1.9.0 norm_1.0-11.1 lifecycle_1.0.3
## [109] BiocManager_1.30.22 jquerylib_0.1.4 MALDIquant_1.22.1
## [112] GlobalOptions_0.1.2 data.table_1.14.8 cowplot_1.1.1
## [115] bitops_1.0-7 httpuv_1.6.11 R6_2.5.1
## [118] pcaMethods_1.90.0 affy_1.76.0 promises_1.2.1
## [121] KernSmooth_2.23-22 codetools_0.2-19 MASS_7.3-60
## [124] gtools_3.9.4 assertthat_0.2.1 chron_2.3-61
## [127] proto_1.0.0 rjson_0.2.21 withr_2.5.0
## [130] GenomeInfoDbData_1.2.9 parallel_4.2.3 hms_1.1.3
## [133] grid_4.2.3 rmarkdown_2.23 shiny_1.7.4.1knitr::knit_exit()